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Paper Abstract and Keywords
Presentation 2024-06-27 13:00
[Invited Lecture] In-Network Inference Assisted by eBPF, XDP, and AF_XDP
Takanori Hara (NAIST), Masahiro Sasabe (Kansai Univ.) CQ2024-33
Abstract (in Japanese) (See Japanese page) 
(in English) Network programmability and softwarization enable advanced traffic processing using machine learning (ML). Most of existing studies have focused on deploying ML on Programming Protocol-independent Packet Processors (P4) switches to achieve fast and advanced In-Network Inference (INI) for core networks. However, operational and capital expenditures hinder the deployment of INI models in network edges. In this study, we investigate the feasibility of INI in network edges by leveraging extended Berkeley Packet Filter (eBPF), eXpress Data Path (XDP), and AF_XDP, which are supported by the Linux kernel. Specifically, eBPF/XDP programs can run directly in the kernel but they must adhere to stringent constraints. To overcome these constraints, we adopt quantized neural networks (NNs). On the other hand, AF_XDP can provide fast packet transfer to the user space, enabling INI without such programming constraints. Through experiments, we evaluate the fundamental characteristics of the proposed approaches in terms of the packet processing rate and inference accuracy. We also discuss their potential applications.
Keyword (in Japanese) (See Japanese page) 
(in English) Network programmability / in-network inference / extended Berkeley Packet Filter (eBPF) / eXpress Data Path (XDP) / AF_XDP / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 90, CQ2024-33, pp. 54-55, June 2024.
Paper # CQ2024-33 
Date of Issue 2024-06-19 (CQ) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF CQ2024-33

Conference Information
Committee CQ  
Conference Date 2024-06-26 - 2024-06-28 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To CQ 
Conference Code 2024-06-CQ 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) In-Network Inference Assisted by eBPF, XDP, and AF_XDP 
Sub Title (in English)  
Keyword(1) Network programmability  
Keyword(2) in-network inference  
Keyword(3) extended Berkeley Packet Filter (eBPF)  
Keyword(4) eXpress Data Path (XDP)  
Keyword(5) AF_XDP  
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Keyword(7)  
Keyword(8)  
1st Author's Name Takanori Hara  
1st Author's Affiliation Nara Institute of Science and Technology (NAIST)
2nd Author's Name Masahiro Sasabe  
2nd Author's Affiliation Kansai University (Kansai Univ.)
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Speaker Author-1 
Date Time 2024-06-27 13:00:00 
Presentation Time 25 minutes 
Registration for CQ 
Paper # CQ2024-33 
Volume (vol) vol.124 
Number (no) no.90 
Page pp.54-55 
#Pages 2 
Date of Issue 2024-06-19 (CQ) 

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